PV Storage System Research Articles

Comparison of economic model predictive control and rule‐based control for residential energy storage systems

This study quantifies the benefits of implementing model predictive control on residential solar PV and energy storage systems considering a time-of-use demand tariff, feed-in tariff and varying PV system sizes and battery life-cycle costs. The control system analysed makes use of economic model predictive control (EMPC) whereby the objective function is directly tied to the economics of the system. Using residential load and PV data from an Australian distribution network service provider, the EMPC controller is compared to a rule-based controller, highlighting the benefits of EMPC in regards to annual economic performance and battery energy throughput. The EMPC algorithm is then tested using 10 residential customers at the low voltage feeder level showing the capacity for the EMPC controller to shift peak demand and flatten the aggregated load profile of 30 residential customers.

Go Real: Power Electronics From Simulations to Experiments in Hours: Versatile Experimental Tool for Next Generation Engineers

The only constant is change-power systems worldwide are going through a paradigm change from centralized generation to distributed generation; transportation systems are being electrified; and billions of lives in third-world countries are awaiting low-cost sustainable electricity. Control and power electronic technologies are two common enablers to address these grand challenges. Empowering next-generation engineers with hands-on skills in control and power electronics has become a priority for global higher education. However, setting up a suitable experimental system requires time, effort, and a broad range of expertise. This article aims to help researchers, university professors, graduate students, and engineers lower the barriers to go real from simulations to experiments for various power electronic systems and improve the efficiency and productivity of research, development, and education. It shows that it is possible to obtain experimental results within hours after completing simulations by adopting the SYNDEM Smart Grid Research and Educational Kit, which is a reconfigurable, opensource, multifunctional power electronic converter with the capability of directly downloading codes from MATLAB/Simulink. This minimizes the time, cost, and efforts needed to develop hardware systems and removes the burden of coding. After briefly introducing the SYNDEM kit and highlighting the automatic code generation capability, two case studies will be illustrated: an ac motor drive and a dc-dc-ac converter for an integrated PV-storage system.

Establishment of Solar –Wind –Biogas –BESS Based Hybrid Micro-grid

The demand for energy increased due to industrialization, urbanization and population growth. In order to meet the demand for energy, Renewable Energy Sources (RES) are exploited because of its advantages such as developed economic growth and sustainability etc. The micro-grid implemented with isolated distributed generators (DGs) of coordinated operation within it. A hybrid system with all available sources of renewable energy such as solar PV, wind, biogas and battery energy storage system (BESS) for the purpose of 24hrs uninterrupted power supply for the no grid or weak grid regions of rural sectors of India has been recently established in IIEST Shibpur. The design and establishment of solar-wind-Biogas with battery energy storage-based hybrid micro-grid system presented. In this paper design, development and installation of micro-grid consisting of 10kWp solar PV plant, 1kW wind generating system, 35 cubic meter bio digester with 15kVA biogas engine and 1kW, 6kWh VRF battery are described.

Performance of a shared solar and battery storage system in an Australian apartment building

This study presents the energy performance of a three unit apartment building in Perth, Western Australia equipped with a shared energy microgrid. Although there has been a dramatic growth of residential rooftop solar PV across Australia, apartment buildings and their occupants are rarely able to access the benefits associated with onsite renewable energy generation and consumption. To address this, an apartment building in Perth was fitted with a PV and battery energy storage system, with metering architecture. The microgrid configuration enabled the sharing of energy between the apartment units. A one year dataset (December 2017-December 2018) obtained from onsite pulse meters was analysed. Load profiles were assessed and grid minimisation was evaluated through self-sufficiency metric. The three unit apartment showed a 22% reduction in average yearly energy consumption against the benchmark. The findings demonstrated an overall 75% dependency of the microgrid on renewables; and suggest that a shared energy microgrid may be more effective than separate supply connections.

The optimal management of the prosumer’s resources via stochastic programming

This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accounting for the inherent uncertainty affecting the main problem parameters (i.e. generation from renewable energy sources and demands). The problem provides the prosumer with the optimal scheduling of the shiftable loads and operations of the available storage systems that minimizes the expected overall electricity cost. Preliminary results, collected on three different categories of residential prosumers, have shown the effectiveness of the proposed approach in terms of cost saving.

Coordinated control strategy for a PV-storage grid-connected system based on a virtual synchronous generator

Due to the characteristics of intermittent photovoltaic power generation and power fluctuations in distributed photovoltaic power generation, photovoltaic grid-connected systems are usually equipped with energy storage units. Most of the structures combined with energy storage are used as the DC side. At the same time, virtual synchronous generators have been widely used in distributed power generation due to their inertial damping and frequency and voltage regulation. For the PV-storage grid-connected system based on virtual synchronous generators, the existing control strategy has unclear function allocation, fluctuations in photovoltaic inverter output power, and high requirements for coordinated control of PV arrays, energy storage units, and photovoltaic inverters, which make the control strategy more complicated. In order to solve the above problems, a control strategy for PV-storage grid-connected system based on a virtual synchronous generator is proposed. In this strategy, the energy storage unit implements maximum power point tracking, and the photovoltaic inverter implements a virtual synchronous generator algorithm, so that the functions implemented by each part of the system are clear, which reduces the requirements for coordinated control. At the same time, the smooth power command is used to suppress the fluctuation of the output power of the photovoltaic inverter. The simulation validates the effectiveness of the proposed method from three aspects: grid-connected operating conditions, frequency-modulated operating conditions, and illumination sudden-drop operating condition. Compared with the existing control strategies, the proposed method simplifies the control strategies and stabilizes the photovoltaic inverter fluctuation in the output power of the inverter.

A Priori Multiobjective Self-Adaptive Multi-Population Based Jaya Algorithm to Optimize DERs Operations and Electrical Tasks

A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid’s capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and $CO_{2}$ emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and $CO_{2}$ emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between $CO_{2}$ emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.

The Integrated Design of a Novel Secondary Control and Robust Optimal Energy Management for Photovoltaic-Storage System Considering Generation Uncertainty

Due to the generation uncertainty of photovoltaic (PV) power generation, it has been posing great challenges and difficulties in maintaining the stability, security, and reliability of PV-storage systems (one kind of microgrid). To overcome these challenges and difficulties, this paper is concerned with secondary control and robust energy management for PVs in a grid-connected microgrid (MG) considering uncertainty. In our designs, to maintain the stable operation of PVs in MG, a novel secondary control method combining an event-triggered finite time sliding mode controller (FTSMC) and consensus controllers is proposed. Furthermore, a robust optimization framework is established to minimize the total cost of grid-connected MG involving the operation cost of multi-battery Energy Storage Systems (BESSes) and the electricity purchased from the main grid. To eliminate the effects of PV uncertainty, the optimization problem with uncertain constraints is converted into a new optimization problem with only deterministic constraints by using the box theory to represent the PV outputs. In other words, the robust optimization strategy makes uncertain boundaries easier to be represented by setting all uncertain parameters into an uncertain domain involving all typical extreme cases. Then, a particle swarm optimization (PSO) method is employed to solve the newly converted optimization problem. Finally, the experimental results validate the effectiveness of the proposed integrated framework.

The Influence of Meteorological Features on the Performance Characteristics of Solar Photovoltaic Storage System

Over the years, there has been increasing interest in exploitation of renewable energy source (RES) as a result of fast depletion of fossil fuel based conventional power generation with attendant negative environmental impact. However, renewable energy sources such as solar, wind, biomass etc are not always available because of their fluctuating nature. In view of this, it has become imperative to have an efficient energy storage system (ESS) for sustained energy availability. Batteries storage often suffers early failure due to irregular charge and discharge cycle which could eventually shorten its life span. Therefore, this research focuses on investigation of the influence of meteorological parameters on battery storage in solar PV system as well as evaluating the influence of these factors on the performance characteristics of solar PV storage system. The work examines particularly the effect of varying solar temperature and irradiance on the system output charging current and overall efficiency. A modified mathematical model of a solar PV was developed to show the relationship between meteorological parameters (irradiance and temperature), and PV output short circuit current. Analysis of meteorological data obtained for determination of state of charge (SOC) of battery storage based on the six geographical areas in Nigeria revealed that, in Bornu state (North-east) the battery attained 100% (hundred percent) state of charge in1Hr (one hour), while in Plateau state (north-central), the battery attained 60% (sixty percent) state of charge in 8hrs (eight hours), being the least in terms of charging rate. In Kaduna (North-west) region, the battery attained 88% state of charge in one hour and 96% SOC after duration of eight hours. This is quite different from Lagos (Southwest), where the battery SOC in one hour was 38% and 78% SOC in eight hours. This phenomenon therefore, revealed that solar PV system implementation should be site specific and it also account for the life span of the storage and the system efficiency.

Development of a Control Platform for a Building-Scale Hybrid Solar PV-Natural Gas Microgrid

Building-scale microgrids are a type of behind-the-meter microgrids where the building operator has control of the distributed energy resources, including, in this case, a natural gas-fired microturbine in addition to solar PV and battery energy storage systems. There is a growing trend in deploying behind-the-meter microgrids due to their benefits including the resiliency of serving critical loads, especially in regions with abundant natural gas. In order to ensure distributed energy resources are dispatched optimally for the desired mode of operation, a hierarchical control platform including a centralized controller was developed and installed. The platform includes communication and control infrastructure that interface with controllers for distributed energy resources and the building automation system of a recently built energy efficient commercial building. Based on desirable outcomes under different grid and building conditions, operational modes were defined for the microgrid controller. The controller is programmed to map each mode to respective operational modes for distributed energy resources controllers. Experimental data for test runs corresponding to two operational modes confirm the communication and control infrastructure can execute hierarchical control commands. Finally, dispatch optimization for a year-long simulation of system operation is presented and the benefits of the hybrid solar PV-natural gas setup are evaluated.

Techno-Economic Analysis of a Residential PV-Storage Model in a Distribution Network

The high penetration level of photovoltaic (PV) generation in distribution networks not only brings benefits like carbon savings, but also induces undesirable outcomes, like more harmonic components and voltage fluctuations. Driven by decreasing costs of energy storage, the focus of this paper is to investigate the feasibility of applying energy storage in the grid-connected PV system to mitigate its intermittency. Firstly, to appreciate the functionality of storage, a generic PV-battery-supercapacitor model was simulated in MATLAB/Simulink, and a flat load profile was obtained to enhance predictability from the network management point of view. However, the usage of supercapacitors at the residential level is limited, due to its high startup costs. Secondly, a detailed residential PV-battery model was implemented in the System Advisor Model (SAM) based on local data in Dubai. The optimal sizing of a battery system was determined by assessing two criteria: The number of excursions, and average target power, which are contradictory in optimization process. Statistical indicators show that a properly sized battery system can alleviate network fluctuations. The proposed sizing method can be also applied to other PV-storage systems. Finally, economic studies of PV-battery system demonstrated its competitiveness against standalone PV systems under appropriate tariff incentives.

“Sizing of PV storage systems for different household types”

This paper presents a novel method of sizing PV storage systems for different household types such as single -, family –shared flats – or pensioner households. The method is based on a simulation model that characterizes the PV system including peripheral components like the inverter and the battery. The required input data to carry out the calculations is gathered from simulated household loads, characteristics of system components and measured radiation values. Sizing the grid-connected PV storage system is performed based on technical parameters only, without economic evaluation. The results show significant differences in the ideal system configuration depending on the household types ranging from a PV to battery ratio of 0.76–4.25 kWpeak/kWh. This suggests, that the household type needs to be considered before installing a PV storage system in order to ensure optimal results for the customer and the energy system. After the technical sizing, the results are compared to economic design optima, in order to investigate existing differences.

A Robust Power Management Strategy With Multi-Mode Control Features for an Integrated PV and Energy Storage System to Take the Advantage of ToU Electricity Pricing

In this paper, a power management strategy (PMS) for an integrated residential solar photovoltaic (PV) and energy storage unit (ESU) is proposed for both grid-connected and islanded operations to take the advantage of time-of-use pricing. This is an effective solution to integrate storage and renewable energy sources, such as solar PV, with the conventional grid to improve the reliability and efficiency of the power system and to reduce the total electricity cost for the consumer. An automatic switching control strategy is proposed to realize a smooth switching among the various operation modes of the proposed energy management strategy. The integrated PV-storage system is composed of a 5-kW PV array, a 3.5-kW·h ESU formed by 12-V single lead acid batteries, and three power converters. The PMS ensures seamless switching of the ESU converter between the charging and the discharging mode and seamless switching between the inverting and the rectifying mode of the grid converter. It also ensures that the local loads are supplied when the grid fails. An experimental setup has been implemented in the laboratory using the real-time field-programmable gate array general-purpose inverter control card from National Instrument to verify the practical feasibility of the proposed PMS under the various scenarios of the PV irradiation, load changings, and the state of charge of the ESU. The results show that the proposed PMS is effective and robust against various scenarios.

Peak shaving and valley filling potential of energy management system in high-rise residential building

In recent years, China has recognized rapidly increasing High-rise Residential Building (HRB) constructions due to the high rate of urbanization. The intensive and variable electricity demand in HRBs exerts large pressure on grid. Pairing Energy Management System (EMS) with PV storage system provides a clean and efficient way to utilize local renewable resources. By dispatching shiftable loads and storage resources, EMS could effectively reshape the electricity net demand profiles and match customer demand and PV generation. In this paper, a Multi-Agent System (MAS) framework is employed to investigate the peak shaving and valley filling potential of EMS in a HRB which is equipped with PV storage system. The effects of EMS on shiftable loads and PV storage resources are analyzed. The simulation results reveal the feasibility of the proposed approach to effectively flatten the HRB electricity demand and net demand profiles. With the help of EMS, the peak-to-valley ratio of demand profiles and net demand profile are reduced significantly.

Potential of Off-grid Solar PV/Biogas Power Generation System: Case Study of Ado Ekiti Slaughterhouse

In Nigeria, some economically important facilities are not functioning optimally due to lack of/inadequate electricity from the national grid. To solve this problem, these facilities result to using diesel generators which are mostly uneconomical and environmentally unfriendly. Improving comparative economic advantage means that Renewable Energy Sources (RES) with reduced emission and long term cost of energy is a viable alternative. This work describes the potentials of using solar PV/biogas power system to supply the slaughterhouse located in Ado Ekiti, South West Nigeria through an optimal design and techno-economic analysis. With an annual average daily radiation of 4.93 kWh/m 2 /d, the facility slaughters an average of 25 cows daily to yield a biomass supply of about 1150 kg/day. Simulation results from HOMER software present an optimal PV/biogas generator/battery/converter system that is able to supply the 164 kWh daily load requirement of the facility. The PV system supplied 38% of the annual total electricity production which stood at 76,384 kWh/yr. Cost analysis also indicates that the system has levelized cost of energy of $ 0.236/kWh and a Net Present Cost (NPC) of $ 92,988. 60% of this cost is shared equally between the PV and battery storage system while the biogas generator and digester and converter cost make up 25% and 15% respectively.

Application of PVStorage System in Typical Industrial Users

The power consumption of industrial enterprises is characterized by large power consumption and high reliability requirement, so the cost of electricity consumption is relatively high. Distributed photovoltaic power generation is clean and environmentally friendly, making full use of the roof area to generate electricity. Based on the characteristics of distributed photovoltaic and energy storage, this paper constructs the distributed optical storage model and operation strategy. In addition, this paper takes an industry as an example to carry out relevant verification and analysis.

Improving Grid Integration of Hybrid PV-Storage Systems Through a Suitable Energy Management Strategy

This paper proposes a method to reshape the daily grid-injected photovoltaic (PV) power profile while keeping the same energy amount. In particular, an energy management algorithm is used for reducing the peak-to-mean ratio (PMR) of the injected power profile. This method allows for a better integration of hybrid PV-storage systems into the power grid since it helps avoiding or at least reducing the need for curtailment measures aimed at preventing grid instability. Furthermore, it is compatible with negotiation schemes that require transmitting the expected power profile to the grid manager one day ahead. Thus, a better market participation of PV producers can be achieved. Five different scenarios, with varying system parameters, have been simulated over a one-year period, showing a reduction of the PMR up to 64% and a reduction of the power ramp rate up to 48%. These results confirm the effectiveness of the proposed approach.

Diagnosis and prognosis of complex energy storage systems: tools development and feedback on four installed systems

Electrical Storage systems are used since many decades in stand-alone applications. Recently, their use in grid connected renewable energies installations increases not only the flexibility of these systems but also their complexity. The optimization of their operation in these systems is necessary for cost effectiveness and is a technical and scientific challenge. The research and development projects are increasing worldwide to tackle different aspects of these new solutions. This paper presents advanced analysis of some of these systems based on new approaches of data analytics. Four PV-Storage systems have been monitored for three years and an original diagnostic and prognostic tool is developed for the analysis of the performance and defaults of such systems.In addition, the generic approach presented in this paper allowed to have a feedback on the performance of grid connected PV-storage systems with two different storage technologies: Li-ion and NaNiCl2. The efficiency analysis includes the performance of batteries and power conversion systems. So, the objective here is to compare the usage and the performance of electrical storage systems along time within each power plant and also between the monitoring power plants using experts’ knowledge and data processing.

Intelligent control of household Li-ion battery storage systems

The increase in electricity prices along with a decrease in the price of storage systems has led to a rapid expansion of the PV-battery home storage system market. In order to be economically viable PV-storage systems must fulfil certain performance criteria, and the system control strategy has a large impact on the overall system performance.At KIT the performance of 20 commercially available PV-battery systems has been evaluated based on several criteria, one of these is intelligent control. A detailed study of the relationship between battery ageing and control strategy of 6 of these systems with NMC-based cells is part of the evaluation. It is shown that an intelligent control strategy can prevent calendar ageing. How large the effect is obviously depends on the dimensioning of the system and the sensitivity of capacity fade at different SOC levels. It is observed that a difference in battery lifetime caused by calendar ageing of up to 1.5 years is possible.In order to complement these results it is interesting to also consider the effects of intelligent control strategies (or the lack thereof) on the PV power fed into the grid. The feed-in of PV power to the grid is often regulated by law, and if the battery is fully charged too early in the day the system operator is forced to throttle the PV-system during the midday peak. An intelligent charging regime with accurate load and generation forecasts can prevent this, but could also lead to a reduction in self-sufficiency if the prediction is incorrect. It is shown that between 62 kWh and 104 kWh per year are lost due to the reason that most storage systems don’t possess a charging strategy to prevent throttling of the PV-generator.Since the effects of battery ageing and unwanted PV throttling are not independent it is useful to evaluate them in parallel and in combination with results on battery and system efficiency. The goal is to determine the economic impact of intelligent control and the resulting interplay between each of these factors.

Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system

In this paper, an intelligent technique for EMS based on Recurrent Neural Network (RNN) with aid of Ant-Lion Optimizer (ALO) algorithm is presented to find energy scheduling in MG. The optimal operation programming of electrical systems through the minimization of production cost as well as better utilization of renewable energy resources, such as the PV system, WT, and storage system. The objective of the proposed method is utilized to the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real-time scheduling. The proposed method is able to analyze the technical and economic time-dependent constraints. The proposed method attempts to meet the required load demand with minimum energy cost. To accomplish this aim, demand response (DR) is evaluated by utilizing the RNN and additional indices for evaluating customer response, such as consumers information based on the offered priority, DR magnitude, duration, and minimum cost of energy (COE). Finally, the ALO algorithm is developed to solve the economic dispatch issues for determining the generation, storage, and responsive load offers. The proposed method is implemented in MATLAB/Simulink working platform and their performances are tested with the existing methods such as GA, ABC, and BFA respectively.